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Kitaru
Replay-based evals for AI agents — traces you can run, not just read.
Day 43 since launch · Launched 25 August 2026
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What is Kitaru?
Kitaru turns production agent traces into reproducible regression tests. Wrap your agent once, or import existing traces from Langfuse, LangSmith, Braintrust, Logfire or Arize Phoenix, then replay any session against your next model, prompt or code change. Replays re-execute your real code with recorded tool-call responses, so an unchanged run gives a faithful baseline and a forked run shows exactly what changed. Human reviewers can pin judgments to exact trace locations, calibrate evaluators, freeze cohorts and run experiments before shipping. It is open source, self-hosted, ships with a Python SDK and TypeScript client, an MCP server, and coding-agent skills for Claude Code and Cursor.
Pros
- Replays real agent code against recorded tool responses for faithful baselines
- Imports traces from most popular observability platforms
- Open source and self-hosted with Python SDK, TypeScript client and MCP server
Cons
- Self-hosting requires infrastructure setup and a worker next to your agent's dependencies
- Most valuable once you already have production traces to replay
Who should use Kitaru
AI engineering teams and agent builders who want regression testing and evals tied to real production traces.
Who may not need it
Teams without existing agent traces or those that only need simple prompt-unit tests.
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